我想从 khung dữ liệu data 读取两列 S1_max 和 S2_max。无论 S1_max 列中存在值，我都想检查每个 S1_max 后面是否有相应的 S2_max 信号。如果是这样，我计算 S1_max Và S2_max 信号之间的时间增量。然后，此结果在单独的 mệnh lệnh d 中的 S2_max 列的 datetime[64ns] 索引处建立索引，然后将其附加到 danh sách delta_data 。如何将此结果添加到相应 datetime[64ns] 索引处已有的 data 数据框中？

这是我创建的delta_data:

#time between each S2 global maxima: 86 ns/samp freq 200 = 0.43 ns
#Checking that each S1 is succeeded by a corresponging S2 signal and calculating the time delta:
delta_data = []
diff_S1 = 0
diff_S2 = 0
i = 0
while((i + diff_S1 + 1 < len(peak_indexes_S1)) and (i + diff_S2<>
# Find next ppg peak after S1 peak
    while (df["S2"].index[peak_indexes_S2[i + diff_S2]] < df["S1"].index[peak_indexes_S1[i+diff_S1]]):
        diff_S2=diff_S2+1

    while (df["S1"].index[peak_indexes_S1[i+diff_S1+1]] < df["S2"].index[peak_indexes_S2[i + diff_S2]]):
        diff_S1=diff_S1+1

    i_peak_S2 = peak_indexes_S2[i + diff_S2]
    i_peak_S1 = peak_indexes_S1[i + diff_S1]

    d={}
    d["td"] = (df["S2"].index[i_peak_S2]-df["S1"].index[i_peak_S1]).microseconds
    d["time"] = df["S2"].index[i_peak_S2]
    PATdata.append(d)

    tôi = tôi + 1

time_delta=pd.DataFrame(delta_data)

delta_data 打印出来:

         td time
0 355000 2019-08-07 13:06:31.010
1 355000 2019-08-07 13:06:31.850
2 355000 2019-08-07 13:06:32.695

đây là của tôidữ liệu数据框:

                           l1 l2 l3 l4 S1 S2 S2_max S1_max

2019-08-07 13:11:21.485 0.572720 0.353433 0.701320 1.418840 4.939690 2.858326 2.858326 NaN
2019-08-07 13:11:21.490 0.572807 0.353526 0.701593 1.419052 4.939804 2.854604 NaN 4.939804

此数据框的创建者:

data = pd.read_csv('file.txt')
data.columns = ['l1','l2','l3','l4','S1','S2']
nbrMeasurments = sum(1 for line in open('file.txt'))
data.index = pd.date_range('2019-08-07 13:06:30'), periods=nbrMeasurments-1, freq="5L")

我尝试过DataFrame.combine_firstVànối thêm.

此外，尝试向 data 添加另一个数据帧时也会出现同样的问题。此数据帧在日期时间框架中没有毫秒:

                     S3 S4 
Ngày                                       
2019-08-07 13:06:30 111 61

câu trả lời hay nhất

据我所知，您正在尝试将另一列附加到现有的 DataFrame 中。

具体操作方法如下:

df1 = pd.DataFrame({'names':['bla', 'blah', 'blahh'], 'values':[1,2,3]})
df2_to_concat = pd.DataFrame({'put_me_as_a_new_column':['row1', 'row2', 'row3']})

pd.concat([df1.reset_index(drop=True), df2_to_concat.reset_index(drop=True)], axis=1)

reset_index(drop=True) 确保您不会生成 NaN 或重复的索引列。

关于python - 如何将包含数据和 datetime64[ns] 的列表与具有 datetime64[ns] 索引的 pandas 数据框合并，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/57494328/